Least-Squares Parameter Estimation for Catalyst Layer Agglomerate Models
Bibliographic record
Abstract
A framework is presented to estimate the micro-structural parameters of cathode fuel cell electrodes by means of a nonlinear least-squares method. This work represents the first attempt in the literature to characterize the structure of the catalyst layer by numerical parameter estimation using a two-dimensional membrane electrode assembly model with an ionomer-filled agglomerate catalyst layer approximation. The framework is developed by coupling a two-dimensional model to an optimization based least-squares algorithm in DAKOTA. The algorithm, NL2SOL, minimizes the sum-of-squares of the residuals for any number of data points and parameters. Employing the proposed methodology allows for accurate characterization of the electrode structure and quantification the quality of the curve fit. Extension of this methodology allows for parameter estimation as novel materials are incorporated into fuel cell construction. Results indicate that curves can be fit using micro-structural and electrochemical parameters consistent with values published in the literature. However, the quality of the fit deteriorates for large data sets over the entire range of operating conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".